• Optoelectronics Letters
  • Vol. 19, Issue 2, 95 (2023)
Wen LI, Sijia HAO*, Hao ZHOU, and Ying and LIU
Author Affiliations
  • Institute of Mechanical and Electrical Engineering, North China University of Technology, Beijing 100144, China
  • show less
    DOI: 10.1007/s11801-023-2127-9 Cite this Article
    LI Wen, HAO Sijia, ZHOU Hao, and LIU Ying. Research on modeling method of continuous spectrum water quality online detection based on random forest[J]. Optoelectronics Letters, 2023, 19(2): 95 Copy Citation Text show less
    References

    [1] WEI K L, CHEN M, WEN Z Y. Research on signal processing for water quality monitoring based on continuous spectral analysis[J]. Spectroscopy and spectral analysis, 2014, 34(12):3368-3373.

    [2] WANG C L, WANG B, JI T. Simulated estimation of nitrite content in water based on transmission spectrum[J]. Spectroscopy and spectral analysis, 2022, 42(07):2181-2186.

    [3] HE M X, LI J, FAN W Y. Correlation between floc morphology and water quality based on partial least squares[J]. The administration and technique of environmental monitoring, 2021, 33(06):48-51.

    [4] YAN W L, REN S Y, YUE X X. Rapid detection of cAMP content in red jujube using near-infrared spectroscopy[J]. Optoelectronics letters, 2018, 14(5): 380-383.

    [5] WANG Y M, CHEN H R, CHEN J Y. Comparation of rice yield estimation model combining spectral index screening method and statistical regression algorithm[J]. Transactions of the Chinese society of agricultural engineering, 2021, 37(21):208-216.

    [6] CASTRILLO M, GARCíA L á. Estimation of high frequency nutrient concentrations from water quality surrogates using machine learning methods[J]. Water research, 2020, 172(C).

    [7] MU H Y. Multi-models combined water quality analyzing based on multi-spectra[D]. Hangzhou:Zhejiang University, 2011.

    [8] LI R N, WANG Q, LIU S M. Water quality warning method based on canonical correlation coefficient and random forest[J]. China environmental science, 2021, 41(09):4457-4464.

    [9] MAHSA M, KHANMOHAMMADI K M, HOSSEIN G. Classification of nanofluids solutions based on viscosity values:a comparative study of random forest, logistic model tree, Bayesian network, and support vector machine models[J]. Infrared physics and technology, 2022, 125:104273.

    [10] NAFOUANTI B N, LI J X, ABBA M N. Prediction on the fluoride contamination in groundwater at the DatongBasin, Northern China:comparison of random forest, logistic regression and artificial neural network[J]. Applied geochemistry, 2021, 132:105054.

    [11] BREIMAN L. Random forests[J]. Machine learning, 2001, 45(1):5-32.

    [12] YUAN Z X. Study on spectral classification model based on random forest[J]. Modern information technology, 2021, 5(07):81-84.

    [13] LI S F, JIA M Z, DONG D M. Fast measurement of sugar in fruits using near infrared spectroscopy combined with random forest algorithm[J]. Spectroscopy and spectral analysis, 2018, 38(06):1766-1771.

    [14] WANG K, WANG J X, XING Z N. Infrared spectrum modeling method based on RF algorithm of improved feature selection[J]. Application research of computers, 2018, 35(10):3000-3002.

    [15] VLI W, LV B B, FU H. Study on denoising of continuous spectrum on-line monitoring signal of water quality with micro-reagents based on HHT[J]. Optoelectronics letters, 2022, 18(2):115-121.

    [16] FANG T J, AMIE A, SHANEEL C. Electrochemical detection of nitrate, nitrite and ammonium for on-site water quality monitoring[J]. Current opinion in electrochemistry, 2022, 32:100926.

    [17] MELISSA T, ALAN K. Assessing the accuracy of nitrate concentration data for water quality monitoring using visual and cell phone quantification methods[J]. Citizen science:theory and practice, 2021, 6(1):2.

    [18] DONG C Y, LI W Z, WANG Z H. An automated flow-batch analyzer based on spectrophotometry for the determination of nitrite[J]. Optoelectronics letters, 2019, 15(5):339-342.

    LI Wen, HAO Sijia, ZHOU Hao, and LIU Ying. Research on modeling method of continuous spectrum water quality online detection based on random forest[J]. Optoelectronics Letters, 2023, 19(2): 95
    Download Citation